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A guide to genome‐wide association analysis and post‐analytic interrogation
Author(s) -
Reed Eric,
Nunez Sara,
Kulp David,
Qian Jing,
Reilly Muredach P.,
Foulkes Andrea S.
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6605
Subject(s) - bioconductor , computer science , interrogation , software , visualization , genome browser , data science , graphics , resource (disambiguation) , genome , data visualization , genomics , data mining , programming language , biology , genetics , computer network , archaeology , gene , history , computer graphics (images)
This tutorial is a learning resource that outlines the basic process and provides specific software tools for implementing a complete genome-wide association analysis. Approaches to post-analytic visualization and interrogation of potentially novel findings are also presented. Applications are illustrated using the free and open-source R statistical computing and graphics software environment, Bioconductor software for bioinformatics and the UCSC Genome Browser. Complete genome-wide association data on 1401 individuals across 861,473 typed single nucleotide polymorphisms from the PennCATH study of coronary artery disease are used for illustration. All data and code, as well as additional instructional resources, are publicly available through the Open Resources in Statistical Genomics project: http://www.stat-gen.org.